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System Biology, Metabolomics, and Breast Cancer: Where We Are and What Are the Possible Consequences on the Clinical Setting

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Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues

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Abstract

The discovery that the metabolism of cancer cells is different from non-malignant cells is not new, this finding was described more than a century ago by O. Warburg. Nevertheless, in the last decade the technologies such as capillary electrophoresis, mass spectroscopy (MS), and proton nuclear magnetic resonance spectroscopy (H-NMR) have allowed deciphering the complexity and heterogeneity underlying the cancer metabolism. These high-performance technologies are generating a large amount of data that requires conceptual schemes and approaches to efficiently extract and physiologically interpret the dynamic spectrum of the metabolome data in cancer samples. Breast cancer is a disease that highlights the need to develop computational schemes to systematically explore the metabolic alterations that support the malignant phenotype in human cells. Hence, systems biology approaches with capacities to integrate in silico modeling and high-throughput data are very attractive for clinicians to make oncological treatment decisions combined with static parameters such as clinical and histopathological variables. In this chapter we present a cutting-edge review, perspectives and scope of how metabolic approaches in breast cancer studies can be used not only to integrate the local and systemic response of the host, but also as a technique to look for metabolic biomarkers by non-invasive and simpler sample procedure in biofluids such as serum, saliva, urine, pleural fluid, breath, and ascites. We discuss how the “metabolic phenotype” approach could contribute to developing a personalized medicine by combining metabolome data and computational modeling to evaluate some clinical variables such as detection of relapses, monitoring and response prediction to treatment and toxicity prediction in patients. Even though some advances have been accomplished, in practice there are many challenges and limits that will have to be broken before the metabolomics can be integrated into the day-to-day clinic. Despite this situation, it is evident that the translational multidisciplinary approach combined with the rapid technological development and the correct data interpretation will bring in the future tools for improving outcomes in the clinical area.

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Abbreviations

ABC:

Advanced breast cancer

AUC:

Area under curve

AUROC:

Area under the receiver operating characteristic curve

CE-TOF/MS:

Capillary electrophoresis time-of-flight mass spectrometry

CE:

Capillary electrophoresis

EBC:

Early breast cancer

GC-MS:

Gas chromatography-mass spectroscopy

HER2:

Human epidermal growth factor receptor 2

H-NMR:

Proton nuclear magnetic resonance spectroscopy

HR-MAS:

High resolution magic angle spinning magnetic resonance spectrometry

LBC:

Localized breast cancer

LC-MS:

Liquid chromatography-mass spectroscopy

LTNBC:

Localized triple negative breast cancer

LTPBC:

Localized triple positive breast cancer

MS:

Mass spectroscopy

NMR:

Nuclear magnetic resonance spectroscopy

no pCR:

No pathologic complete response

OPLS-DA:

Orthogonal least-squares discriminant analysis

OS:

Overall survival

pCR:

Pathologic complete response

PLS-DA:

Partial least squares discriminant analysis

TNBC:

Triple negative breast cancer

TNMc:

Clinical tumor/nodes/metastasis

TNMp:

Pathological tumor/nodes/metastasis

TPBC:

Triple positive breast cancer

TT:

Treatment toxicity

TTP:

Time to disease progression

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Acknowledgments

This paper was supported by an internal grant from the Instituto Nacional de Medicina Genomica, Mexico. Meztli L. Matadamas-Guzman is a doctoral student from Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autonoma de México (UNAM) and received fellowship 595252 from CONACYT.

Author information

Authors and Affiliations

  1. Hematology and Oncology Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, México City, Mexico

    Alejandra Armengol-Alonso

  2. Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), México City, Mexico

    Meztli L. Matadamas-Guzman & Osbaldo Resendis-Antonio

  3. Red de Apoyo a la Investigación-Coordinación de la Investigación, Científica-Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico

    Osbaldo Resendis-Antonio

  4. Centro de Ciencias de la Complejidad-UNAM, México City, Mexico

    Osbaldo Resendis-Antonio

Authors
  1. Alejandra Armengol-Alonso
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Correspondence to Osbaldo Resendis-Antonio .

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Editors and Affiliations

  1. Universidad Autónoma de la Ciudad de México (UACM), Mexico City, Mexico

    Luis Olivares-Quiroz

  2. Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), México City, Mexico

    Osbaldo Resendis-Antonio

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Armengol-Alonso, A., Matadamas-Guzman, M.L., Resendis-Antonio, O. (2018). System Biology, Metabolomics, and Breast Cancer: Where We Are and What Are the Possible Consequences on the Clinical Setting. In: Olivares-Quiroz, L., Resendis-Antonio, O. (eds) Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues. Springer, Cham. https://doi.org/10.1007/978-3-319-73975-5_9

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